import chart_studio.plotly as py
import plotly.figure_factory as ff
import pandas as pd
import numpy as np
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot

init_notebook_mode(connected=True) 

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

table = ff.create_table(df)
iplot(table, filename='jupyter-table1')

init_notebook_mode(connected=True) 
data = [go.Bar(x=df.School,
            y=df.Gap)]

iplot(data, filename='jupyter-basic_bar')

data[0]

Bar({
    'x': array(['MIT', 'Stanford', 'Harvard', 'U.Penn', 'Princeton', 'Chicago',
                'Georgetown', 'Tufts', 'Yale', 'Columbia', 'Duke', 'Dartmouth', 'NYU',
                'Notre Dame', 'Cornell', 'Michigan', 'Brown', 'Berkeley', 'Emory',
                'UCLA', 'SoCal'], dtype=object),
    'y': array([58, 55, 53, 49, 47, 40, 37, 36, 35, 33, 31, 30, 27, 27, 27, 22, 20, 17,
                14, 14,  9])
})

init_notebook_mode(connected=True) 

trace_women = go.Bar(x=df.School,
                  y=df.Women,
                  name='Women',
                  marker=dict(color='#ffcdd2'))

trace_men = go.Bar(x=df.School,
                y=df.Men,
                name='Men',
                marker=dict(color='#A2D5F2'))

trace_gap = go.Bar(x=df.School,
                y=df.Gap,
                name='Gap',
                marker=dict(color='#59606D'))

data = [trace_women, trace_men, trace_gap]

layout = go.Layout(title="Average Earnings for Graduates",
                xaxis=dict(title='School'),
                yaxis=dict(title='Salary (in thousands)'))

fig = go.Figure(data=data, layout=layout)

iplot(fig, filename='jupyter-styled_bar')

init_notebook_mode(connected=True) 

s = np.linspace(0, 2 * np.pi, 240)
t = np.linspace(0, np.pi, 240)
tGrid, sGrid = np.meshgrid(s, t)

r = 2 + np.sin(7 * sGrid + 5 * tGrid)  # r = 2 + sin(7s+5t)
x = r * np.cos(sGrid) * np.sin(tGrid)  # x = r*cos(s)*sin(t)
y = r * np.sin(sGrid) * np.sin(tGrid)  # y = r*sin(s)*sin(t)
z = r * np.cos(tGrid)                  # z = r*cos(t)

surface = go.Surface(x=x, y=y, z=z)
data = [surface]

layout = go.Layout(
    title='Parametric Plot',
    scene=dict(
        xaxis=dict(
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            showbackground=True,
            backgroundcolor='rgb(230, 230,230)'
        ),
        yaxis=dict(
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            showbackground=True,
            backgroundcolor='rgb(230, 230,230)'
        ),
        zaxis=dict(
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            showbackground=True,
            backgroundcolor='rgb(230, 230,230)'
        )
    )
)

fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='jupyter-parametric_plot')

init_notebook_mode(connected=True) 

data = [dict(
        visible = False,
        line=dict(color='#00CED1', width=6),
        name = '𝜈 = '+str(step),
        x = np.arange(0,10,0.01),
        y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]
data[10]['visible'] = True

steps = []
for i in range(len(data)):
    step = dict(
        method = 'restyle',
        args = ['visible', [False] * len(data)],
    )
    step['args'][1][i] = True # Toggle i'th trace to "visible"
    steps.append(step)

sliders = [dict(
    active = 10,
    currentvalue = {"prefix": "Frequency: "},
    pad = {"t": 50},
    steps = steps
)]

layout = dict(sliders=sliders)
fig = dict(data=data, layout=layout)

iplot(fig, filename='Sine Wave Slider')